code: def learn_from_batch(self, batch):
      # perform on-policy training iteration
      total_loss, losses, unclipped_grads = self._learn_from_batch(
          batch
      )
  
      if (
          self.ap.algorithm.ratio_of_replay > 0
          and self.memory.num_transitions()
          > self.ap.algorithm.num_transitions_to_start_replay
      ):
          n = np.random.poisson(self.ap.algorithm.ratio_of_replay)
          # perform n off-policy training iterations
          for _ in range(n):
              new_batch = Batch(
                  self.call_memory(
                      "sample",
                      (
                          self.ap.algorithm.num_steps_between_gradient_updates,
                          True,
                      ),
                  )
              )
              result = self._learn_from_batch(new_batch)
              total_loss += result[0]
              losses += result[1]
              unclipped_grads += result[2]
  
      return total_loss, losses, unclipped_grads
  
imports: from typing import Union
  import numpy
  from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import ACERPolicyHeadParameters, QHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters
  from rl_coach.core_types import Batch
  from rl_coach.exploration_policies.categorical import CategoricalParameters
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.spaces import DiscreteActionSpace
  from rl_coach.utils import eps, last_sample
label_desc: Use image node_id 4 for calling the ACERAgent obj's underlying member method code with example usage: obj.learn_from_batch(batch) and returns: total_loss,  losses,  unclipped_grads